Gut microbiota is the key controller of healthy aging. Hypertension and osteoarthritis (OA) are two frequently co-existing age-related pathologies in older adults. Both are associated with gut microbiota dysbiosis. Hereby, we explore gut microbiome alteration in the Deoxycorticosterone acetate (DOCA)-induced hypertensive rat model. Captopril, an anti-hypertensive medicine, was chosen to attenuate joint damage. Knee joints were harvested for radiological and histological examination; meanwhile, fecal samples were collected for 16S rRNA and shotgun sequencing. The 16S rRNA data was annotated using Qiime 2 v2019.10, while metagenomic data was functionally profiled with HUMAnN 2.0 database. Differential abundance analyses were adopted to identify the significant bacterial genera and pathways from the gut microbiota. DOCA-induced hypertension induced p16INK4a+ senescent cells (SnCs) accumulation not only in the aorta and kidney (p < 0.05) but also knee joint, which contributed to articular cartilage degradation and subchondral bone disturbance. Captopril removed the p16INK4a + SnCs from different organs, partially lowered blood pressure, and mitigated cartilage damage. Meanwhile, these alterations were found to associate with the reduction of Escherichia-Shigella levels in the gut microbiome. As such, gut microbiota dysbiosis might emerge as a metabolic link in chondrocyte senescence induced by DOCA-triggered hypertension. The underlying molecular mechanism warrants further investigation.

Objectives To develop a deep convolutional neural network (CNN) for the segmentation of femur and tibia on plain x-ray radiographs, hence enabling an automated measurement of joint space width (JSW) to predict the severity and progression of knee osteoarthritis (KOA). Methods A CNN with ResU-Net architecture was developed for knee X-ray imaging segmentation. The efficiency was evaluated by the Intersection over Union (IoU) score by comparing the outputs with the annotated contour of the distal femur and proximal tibia. By leveraging imaging segmentation, the minimal and multiple JSWs in the tibiofemoral joint were estimated and then validated by radiologists’ measurements in the Osteoarthritis Initiative (OAI) dataset using Pearson correlation and Bland–Altman plot. The estimated JSWs were deployed to predict the radiographic severity and progression of KOA defined by Kellgren-Lawrence (KL) grades using the XGBoost model. The classification performance was assessed using F1 and area under receiver operating curve (AUC). Results The network has attained a segmentation efficiency of 98.9% IoU. Meanwhile, the agreement between the CNN-based estimation and radiologist’s measurement of minimal JSW reached 0.7801 (p < 0.0001). Moreover, the 32-point multiple JSW obtained the highest AUC score of 0.656 to classify KL-grade of KOA. Whereas the 64-point multiple JSWs achieved the best performance in predicting KOA progression defined by KL grade change within 48 months, with AUC of 0.621. The multiple JSWs outperform the commonly used minimum JSW with 0.587 AUC in KL-grade classification and 0.554 AUC in disease progression prediction. Conclusion Fine-grained characterization of joint space width of KOA yields comparable performance to the radiologist in assessing disease severity and progression. We provide a fully automated and efficient radiographic assessment tool for KOA.